A typical stereo matching network requires at least two images as input, which is not directly applicable for a monocular input setting.
However, if one or more novel views can be synthesized with high quality, these additional views can be utilized to train a stereo matching network. Luo et al. [1]} first proposed the two-stage pipeline for perspective images where a novel right view is synthesized at the first stage and paired with the original view to the second stereo stage.
Later, two-stage approaches [2]} mostly followed this work to generate a coarse disparity/depth, and to synthesize novel views via image warping or Depth-Image-Based Rendering (DIBR). Taking the original and the synthesized views as input, the final depth generated from the later stereo matching stage of these approaches shows a significant improvement over the one-stage counterparts.
